Apparatus and methods for generating data structures to represent and compress data profiles

ABSTRACT

Embodiments described herein relate generally to apparatuses and methods for structuring and processing data. In some embodiments, a method includes receiving stimulus-response data, via a processor, the stimulus-response data including a digital representation of a stimulus and a digital representation of a response. The processor calculates a weight associated with the stimulus-response data, based on a rule, and identifies: (1) a distribution type, based on the digital representation of the stimulus; and (2) a range of inclination values of the distribution type, based on the digital representation of the response. The processor compiles a compressed multidimensional data profile associated with an object of the stimulus-response data and based on the weight, the digital representation of the distribution type, and the digital representation of the range of inclination values.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.16/281,843, filed on Feb. 21, 2019 and titled “Apparatus and Methods forGenerating Data Structures to Represent and Compress Data Profiles,”which is a Continuation of U.S. patent application Ser. No. 15/092,349,filed on Apr. 6, 2016, now U.S. Pat. No. 10,255,700, and titled“Apparatus and Methods for Generating Data Structures to Represent andCompress Data Profiles,” the entire contents of each of which are herebyincorporated by reference.

This application is related to co-pending U.S. Pat. No. 10,373,074,issued on Aug. 6, 2019 and titled “Adaptive Correlation of User-SpecificCompressed Multidimensional Data Profiles to Engagement Rules,” theentire contents of which are hereby incorporated by reference.

COPYRIGHT NOTICE

This application may contain material that is subject to copyright, maskwork, and/or other intellectual property protection. The respectiveowners of such intellectual property have no objection to the facsimilereproduction of the disclosure by anyone as it appears in publishedPatent Office file/records, but otherwise reserve all rights.

FIELD

One or more embodiments described herein relate generally to apparatusesand methods for the representation and compression of data using datastructures, and more particularly, to the representation and processingof profile data.

BACKGROUND

Personality profiles of individuals, or “individual mindsets,” havetraditionally been described in qualitative terms, rather than inquantitative terms. Qualitative descriptions, however, cannot quantifythe information upon which they are based, nor can they quantify thelevels of uncertainty associated with such information.

SUMMARY

Embodiments described herein relate generally to apparatuses and methodsfor structuring and processing data, for example individual mindset datain the context of leadership development. In some embodiments, a methodincludes receiving stimulus-response data, at a processor, from anindividual (or “user”). The stimulus-response data includes a digitalrepresentation of a stimulus and a digital representation of a responsereceived from the individual (e.g., via a user interface) in response tothe stimulus. The processor calculates a weight associated with thestimulus-response data based on a rule, and identifies: (1) adistribution type, or “mindset dimension,” based on the digitalrepresentation of the stimulus; and (2) a range of inclination values,or “leanings,” of the distribution type, based on the digitalrepresentation of the response. The processor then compiles a compressedmultidimensional data profile, or “mindset profile,” associated with anobject of the stimulus-response data (i.e., the individual) based on theweight, the digital representation of the distribution type, and thedigital representation of the range of inclination values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system block diagram of a data compressionapparatus, according to an embodiment.

FIG. 2 is a flow diagram illustrating a method for data compression,according to an embodiment.

FIG. 3 is a flow diagram illustrating a method of inclination valuerange, or “mindset leaning” distribution, computation, according to anembodiment.

FIG. 4 illustrates a multidimensional data profile, or “mindsetprofile,” according to an embodiment.

FIG. 5 is an example of a plot illustrating evidence aggregation.

FIG. 6 is an example of a plot of an inclination value range, or“leaning distribution.”

FIG. 7 is an example of a plot of an inclination value range.

FIG. 8A illustrates an example of a time-varying inclination valueranges.

FIG. 8B is an example of a plot of three evolving inclination valueranges, which collectively can define a compressed multidimensional dataprofile.

DETAILED DESCRIPTION

Some embodiments described herein relate generally to representinginformation about individual mindsets in a compressed, multidimensionaldigital format that can be shared among entities and that can bedynamically modified in response to additional information. Methodspresented herein generate digital representations of compressedmultidimensional data profiles, or “mindset profiles,” that quantify notonly information about an individual's mindset, but also the strength ofevidence upon which the information is based, as well as the level ofuncertainty associated with the mindset information. Methods describedherein also allow for the processing and integration of additionalevidence to modify compressed multidimensional data profiles in aconsistent manner.

FIG. 1 illustrates a system block diagram of a data compressionapparatus, according to an embodiment. As shown in FIG. 1, the datacompression apparatus 100 includes a user interface 101 operably coupledto a translation processor 103. The user interface 101 is configured todisplay a set of stimuli to an individual and to receive from the user aset of responses associated with the set of stimuli. The user interface101 can be implemented as or can include one or more of a softwareapplication (“app”), a graphical user interface (GUI), and an inputdevice such as a touchscreen, keyboard, mouse, keypad, etc. The userinterface 101 can run on and/or be accessible via a user device (notshown) such as a desktop or laptop computer, or a mobile device such asa Blackberry or an iPhone. The translation processor 103 can beimplemented using a microprocessor, an application-specific integratedcircuit (ASIC), a central processing unit (CPU), a general purposeprocessor, etc. The translation processor 103 can be configured toperform operations on data received from an individual via the userinterface 101. The translation processor 103 can also be configured tocollect data from the user device, for example by communicating with oneor more software applications running on the user device (e.g., anemail/calendar software such as Microsoft Outlook®, web conferencingsoftware such as Cisco WebEx®, etc.). The translation processor 103 and,optionally, the user interface 101, are operably coupled to a memory105, which may include a random access memory (RAM), a read only memory(ROM) device, a magnetic and/or optical recording medium and itscorresponding drive, and/or another type of static and/or dynamicstorage device that may store information and instructions for executionby translation processor 103. Memory 105 can stores processor-issuableinstructions 105A as well as data including stimuli 105B, responses105C, weights 105D, distribution types 105E, inclination value ranges105F, and compressed multidimensional data profiles 105G, for example inone or more databases. The processor-issuable instructions (orprocessor-readable instructions) can be stored in memory 105 and cancause the translation processor 103 to perform processes describedherein.

Stimuli 105B can include data that can be presented or displayed to anindividual via the user interface to generate or trigger an action orresponse from the individual, for example, a survey-type question,interactive form or graphic, prompt, pop-up window, calendar reminder,email, meeting request, hyperlink, news article, and/or the like.Responses 105C can include data that is received via the user interface101 in response to one or more of the stimuli 105B, for example within apredetermined time period after the one or more stimuli 105B arepresented/displayed via the user interface 101. Response data 105C caninclude data input by a user/individual action via the user interface101 (e.g., typed text, voice-communicated text, touchscreen input datasuch as swipes, mouse clicks, etc.), for example where suchuser/individual actions include answers to questions, minimizing orclosing a pop-up window, accepting or declining a meeting request, etc.In other words, the response data 105C can be any type of response bythe user/individual via the user interface 101 in response to stimuli105B. Response data 105C can also include an indication of the timeelapsed between the associated stimulus 105B and the response 105C,and/or a manner of input of the response 105C (typed text,voice-communicated text, touchscreen input data such as swipes, mouseclicks, etc.). Each of the responses 105C can be paired with (or linkedor associated with) an associated stimulus 105B and, optionally, storedin the memory 105 as a stimulus-response pair. Translation processor 103can calculate (or determine) a weight 105D having a value (e.g., a valuebetween 0 and 1) for each stimulus-response pair, assign that weight toa stimulus-response pair, and used that weight in the computation of oneor more of the compressed multidimensional data profiles 105G. Weights105D can be stored within stored memory 105.

As used herein, a compressed multidimensional data profile 105G, or“mindset profile,” is a vector representation of an individual'spreferences or “leanings” (or behavioral leanings) (also referred toherein as inclination value ranges 105F) associated with each of a setof attitudinal factors or “mindset dimensions” of interest (alsoreferred to herein as distribution types 105E). The compressedmultidimensional data profile 105G is generated through a series ofinteractions of the individual with the user interface 101, and can bebased in part on the relative importance that the individual assigns foreach distribution type 105E. For example, the relative importanceassigned to a particular distribution type 105E can be based on (e.g.,proportional to) the number of interactions by the user in building upor used as input to form a behavioral leaning (inclination value range105F). The set or collection of distribution types 105E used to generatea compressed multidimensional data profile 105G can be case-specific orcustomized to the individual. For example, when performed as part of aleadership development program, generating a compressed multidimensionaldata profile 105G may include the pre-selection (e.g., by auser/individual) and analysis of the following distribution types 105E,described in Table 1 below:

-   -   Action orientation    -   Openness    -   Verification style    -   Judgment style    -   Self-awareness

TABLE 1 Table of Examples of Distribution 105E Types and AssociatedLeaning Labels Learning Labels for Distribution Type Descriptionextremes of the range Action orientation Predisposition to takePassive - Active action Openness Willing to accept Skeptical- Acceptingdifferent ideas and opinions Verification Approach to verifying Personalexperience - and validating Recommendations Judgment style Style ofmaking Slow to judge - Quick judgments about to judge persons or thingsSelf-awareness Awareness of one's Very unaware - Very own strengths andaware weaknesses

For each distribution type 105E, an individual can be represented by aninclination value range 105F, which is a numerical distribution whoseendpoints (i.e., maximum and minimum values) are defined, for example,by the “leaning labels” set forth in Table 1 above. For example, the“Passive-Active” range associated with the distribution type 105E of“action orientation” can span a numerical range of 1 to 10, where 1corresponds to “passive” and 10 corresponds to “active.”

A compressed multidimensional data profile 105G can include a set ofinclination value ranges 105F, and is generated using “evidence” drawnfrom behaviors of individuals. As used herein, “evidence” is abehavioral choice or action of an individual where that behavioralchoice or action has been translated into a stimulus-response signal(i.e., a pairing of one or more stimuli 105B of FIG. 1 with one or moreassociated responses 105C) and mapped to a distribution type (e.g.,distribution types 105E in FIG. 1), for example, based on a predefinedset of rules. For example, if an individual responds to a question (or“stimulus”) regarding whether he/she had considered a leadershipdevelopment program in the past with a response of “yes,” thequestion-answer pair (or stimulus-response pair) is translated into apositive stimulus-response signal that is mapped to a distribution typeof “desire to improve” (also referred to as “openness” as shown in Table1). Evidence can be based on the occurrence of any action taken by anindividual, whether such an action is taken in response to an explicitstimulus or not (e.g., scheduling a meeting without being prompted,providing feedback to an employee without being prompted, etc.). Throughengagement of the individual with the apparatus 100, a set ofstimulus-response signals is generated, and each stimulus-responsesignal of the set of stimulus-response signals is mapped to one or moreassociated inclination values 105F of one or more compressedmultidimensional data profiles 105G as described above.

FIG. 2 is a flow diagram illustrating a method for data compression,compatible with the data compression apparatus 100 of FIG. 1, accordingto an embodiment. As shown in FIG. 2, a method 200 includes receiving,at a processor (such as translation processor 103 of FIG. 1, for examplevia a user interface 101), stimulus-response data (or “evidence”)associated with an individual at 202 and including a digitalrepresentation of a stimulus and a digital representation of a responseassociated with the stimulus. The processor calculates a weightassociated with the stimulus-response data, based on a rule (or set ofrules), at 204. The rule can be based on a credibility of thestimulus-response data, a credibility of a source of thestimulus-response data, and/or a level of uncertainty inherent in apre-existing multidimensional profile for the individual. Thecredibility of the stimulus-response data or the credibility of thesource of the stimulus-response data can be determined by theuser/individual associated with the stimulus-response data. For example,stimulus-response data based on hearsay can have a lower credibility(and an associated lower weight) than stimulus-response data based onobservations. The weight assigned to stimulus-response data can be basedon an analytical/formulaic assessment of the credibility, based on asubjective assignment by the user/individual or based on a combination.For example, credibility of stimulus-response data initially can besubjectively assigned by a user/individual (and then an associatedweight subsequently assigned), and later then subjective assignment ofcredibility by the user/individual can be calculated (or determined) byan analytical/formulaic assessment of the credibility (and associatedweight).

At 206, the processor identifies and/or retrieves from memory a firstdistribution type based on and/or associated with the stimulus. Theprocessor then identifies and/or retrieves from memory a first range ofinclination values based on the digital representation of the response,at 208. Based on the weight, the first distribution type, and the firstrange of inclination values, the processor assembles or compiles acompressed multidimensional data profile at 214. In some embodiments,prior to the processor compiling a compressed multidimensional dataprofile at 214, the processor identifies a second distribution typebased on and/or associated with the digital representation of thestimulus, at 210, and identifies a second range of inclination valuesbased on the response, at 212. In other words, a single stimulus canhave more than one distribution type associated with it. In such cases,the processor then compiles a compressed multidimensional data profileat 214 based on the weight, the first distribution type, the seconddistribution type, the first range of inclination values, and the secondrange of inclination values.

In some embodiments, the stimulus-response data received at theprocessor during method 200 is a first stimulus-response data, and themethod 200 further includes receiving a second stimulus-response dataincluding a digital representation of a second stimulus and a digitalrepresentation of a second response. In such cases, the processor canmodify the compressed multidimensional data profile based on the secondstimulus-response data.

In some embodiments, the processor is configured to present a series ofstimuli to an individual via the user interface, and to receive a seriesresponses via the user interface in response to the series of stimuli,thereby generating a relatively large number of stimulus-response pairdata. The processor can then: (1) calculate a set of weights, eachweight of the set of weights being uniquely associated with astimulus-response pair from the series of stimulus-response pairs; (2)retrieve one or more distribution types (e.g., from memory), eachassociated (in some implementations, uniquely) with a stimulus from theseries of stimuli; (3) retrieve one or more ranges of inclinationvalues, each associated (in some implementations, uniquely) with aresponse from the series of responses and its associated distributiontype; and (4) define a compressed multidimensional data profile based onthe set of weights, the one or more distribution types, and the one ormore ranges of inclination values.

An inclination value range, or “mindset leaning distribution,” is arepresentation of an individual's attitudes or leanings for a givendistribution type (i.e., along a given mindset dimension). Inclinationvalue ranges are computed by aggregating weighted stimulus-response datainto numerical values using pre-specified mappings, thereby transformingthe stimulus-response data into a compressed form that can beefficiently processed. To transform stimulus-response data intoinclination value ranges, each stimulus, or event/situation that theindividual responded to, is mapped to one or more distribution types,and each response (or group of responses) to a particular stimulus ismapped to a range of inclination values along the one or moredistribution types. In some embodiments, inclination value ranges arerepresented as normalized distributions over numerical ranges.Inclination value ranges can have distributions that are unimodal ormultimodal. A method of generating an inclination value range, accordingto an embodiment, is shown in FIG. 3.

As shown in FIG. 3, one or more stimulus-response signals are received(e.g., at a processor and via a user interface), based on an action (orinaction) of an individual, at 316. Each stimulus-response signal isassigned a weight via a translation processor, at 318, based on one ormore rules for assigning weights 320. The rules for assigning weights320 can specify predetermined weights associated with a stimulus and/ora response of the stimulus-response signal. Alternatively or inaddition, the rules for assigning weights 320 can assign or modifyweights based on a source of the stimulus-response signal, apredetermined credibility of the source of the stimulus-response signal,or on a level of uncertainty of a current associated compressedmultidimensional data profile and/or associated inclination value range.The translation processor looks up one or more distribution types (alsoreferred to herein as “mindset dimensions”) associated with a stimulusof the stimulus-response signal, at 322, by accessing astimulus-distribution type map 324 stored in memory. Thestimulus-distribution type map 324 (also referred to as a“stimulus-dimension map”) can be implemented, for example, in a lookuptable that links/maps a particular stimulus of (or stimulus value from)the stimulus-response signal to a particular distribution type (“mindsetdimension). The translation processor also looks up one or moreinclination values associated with a response of the stimulus-responsesignal, at 326, by accessing a response-inclination value map 328 (e.g.,stored in memory). The response-inclination value map 328 can also beimplemented, for example, in a lookup table that links/maps a particularresponse of (or response value from) the stimulus-response signal to aparticular inclination value (leaning value). One or more currentinclination value ranges associated with the individual and with thestimulus-response signal is retrieved or “read,” at 330, from a table ofcurrent inclination value ranges 332 stored in the memory. At 334, thetranslation processor adjusts the one or more inclination value rangesretrieved at 330, based on the weight assigned at 318, the distributiontype(s) retrieved at 322, the inclination value(s) retrieved at 326, andthe current inclination value range(s) retrieved at 330. The adjustedinclination value range(s) are then stored in the memory, for example byreplacing what was previously the “current inclination value range(s),”or by adding the adjusted inclination value range(s) as a separaterecord in the memory. The adjusted inclination value range(s),optionally in combination with other previously stored inclination valuerange(s), can subsequently be used by the translation processor togenerate an adjusted compressed multidimensional data profile.

An individual's compressed multidimensional data profile includes acollection or consolidation of inclination value ranges along multipledistribution types. FIG. 4 is a graphical illustration of a compressedmultidimensional data profile, according to an embodiment. As shown inFIG. 4, the compressed multidimensional data profile includes threeinclination value ranges (or “leaning distributions”)—one for each ofthree distribution types: action orientation (top), openness (middle),and judgment style (bottom). The individual represented by thecompressed multidimensional data profile of FIG. 4 can generally bedescribed as action-oriented, open to accepting new ideas, and quick tomake judgments. Each of these attributes is quantified by the associatedinclination value ranges along a numerical continuum, the inclinationvalue ranges each including data that is normally distributed andcentered about an inclination value that reflects the latest aggregationof processed stimulus-response data for the individual.

In some embodiments, a compressed multidimensional data profile isdefined as:

M(I)={RI(DT1,I),RI(DT2,I),RI(DT3,I) . . . RI(DTN,I)},

where M=the compressed multidimensional data profile, I=an identifier ofan individual, RI=a range of inclination values, and DT1 through DTNrepresent distribution types of a set of distribution types. Forexample, if there are four distribution types—desire to improve (DES),willingness to learn (WIL), judgment style (JUD), and learning style(LRN) and the range of inclination values for an individual I alongcompressed multidimensional data profile M is expressed by the notationRI(M,I), then the mindset profile of the individual I is represented as:

M(I)={RI(DES,I),RI(WIL,I),RI(JUD,I),RI(LRN,I)}

As discussed above, the range of inclination values RI is defined as acontinuous distribution along a numerical range.

When a new stimulus-response signal (or other evidence) that is mappedto one or more distribution types is received, the corresponding rangeof inclination values is modified according to the following function:

RI¹(X,I)=RI⁰(X,I)(+)SD(X,I)

where:

-   -   RI⁰(X,I) is the current leaning distribution;    -   RI¹(X, I) is the modified leaning distribution;    -   SD (X,I) is the leaning distribution of the signal;    -   X is a vector representing the range of values of a dimension        (also referred to herein as a distribution type such as those        shown in Table 1); and    -   (+) is the notation for evidence aggregation.

As discussed above, stimulus-response signals (or other evidence) aremapped onto distribution types with specific associated inclinationvalue ranges. Other evidence can be, for example, demographicinformation of the user/individual; a younger user/individual may bestatistically more likely to be open minded and can be more likelymapped onto a distribution type such as “Openness” listed in Table 1.Any event in which an individual interacts with the systems describedherein (e.g., via the user interface), or even the lack of such an event(e.g., an individual fails to schedule a meeting within a certain amountof time, an individual fails to provide a meeting summary within acertain amount of time, or an individual fails to follow up on an actionitem identified during a meeting within a certain amount of time), canbe translated into a stimulus-response signal such that the stimulus canbe mapped to one or more distribution types, and the response associatedwith the stimulus can be mapped to a range of one or more inclinationvalues (i.e., a subset of all available inclination values). Thistranslation of user events into inclination values for one or moredistribution types may be referred to as “evidence aggregation.” FIG. 5is a plot illustrating evidence aggregation, according to an embodiment.With reference to FIG. 5, the “current leaning distribution” curve showsa starting distribution or inclination value range. This startingdistribution or inclination value range can be a generic to allindividuals or selected specifically for the given individual. The“leaning distribution from evidence” is a representation of aninclination value range that is based on evidence that has been receivedand translated. The “non-normalized aggregation” curve is anintermediate product representing the weighted aggregation of the“current leaning distribution” and the “leaning distribution fromevidence.” The “aggregated leaning distribution” curve is a normalizedversion of the “non-normalized aggregation” curve data. In other words,the “aggregated leaning distribution” curve is a modified version of theinitial “current leaning distribution” after evidence (e.g., one or morestimulus-response pairs) has been received, processed/translated, andused to adjust what was previously the “current leaning distribution.”For each curve shown in FIG. 5, the maximum value is set to 1 and allother values are scaled accordingly.

In some embodiments, an inclination value range is represented as avector of two arrays. In other words, rather than representing orstoring the inclination value range as a graph as shown in the exampleof FIG. 5, the inclination value range can be compressed and stored in acompressed form as a vector of two arrays. The first array has twovalues—weightage and interval, and the second array has values for eachincrement by interval between 1 and 10. A value greater exceeding 1represents the number of zeroes following the previous increment. Theprecision of the data can be inversely proportional to the incrementvalue chosen. In some embodiments, an inclination value range satisfiesone or more of the following conditions:

-   -   There should be two valid arrays    -   Conditions for validating the first array:        -   Array contains two non-negative values.        -   The first value—weightage—is a non-negative, real number.        -   The second value—increment—is a number that is greater than            0 such that 10 divided by the increment is an integer.    -   Conditions for validating the second array:        -   All values are non-negative.        -   At least one element in the array has a value of 1.        -   All values >1 are positive integers.        -   (The number of values <=1)+(sum of values >1)=10÷increment.

In some embodiments, inclination value ranges are stored in memory asarrays of linear segments of values between 0 and 1 for increments ofleaning values between 1 and 10. For example, in some implementations,an increment of 0.1 is used, and the inclination value range isrepresented within the system as an array of 100 values (e.g.,collectively defining or representing a compressed version of a curvesuch as those shown in FIG. 5) with a header array that specifies theweightage and increment. A graphical representation of an exampleinclination value range, defined as{{5,0.1},{3,0.1,0.2,0.3,0.7,1.0,0.8,0.2,90}, has a unimodal distributionand is shown in FIG. 6. A further example inclination value range,having a bimodal distribution and defined as:

-   -   {{4,0,2},{25,0.1,0.3,0.7,1.0,1.0,0.8,0.7,0.5,0.7,0.9,0.8,0.3,0.2,0.1,11}}        is shown in FIG. 7.

Given one or more inclination value ranges, such as those shown anddiscussed with reference to FIGS. 6 and 7, a compressed multidimensionaldata profile can include a collection of the one or more inclinationvalue ranges (or a subset thereof), each associated with a distributiontype, for example:

-   -   {{{5,0.1},{3,0.1,0.2,0.3,0.7,1.0,0.8,0.2,90}    -   {{4,0.2},{2,0.1,0.3,0.5,0.7,1.0,0.9,0.8,0.5,39}    -   {{2,0.1},{30,0.1,0.2,0.3,0.7,1.0,0.8,0.2,63}}

In some embodiments, the modification of inclination value rangesinvolves modifying an existing or previously-stored inclination valuerange based on a newly-received stimulus-response signal (or otherevidence), for example by adding or removing the effects of the newstimulus-response signal to/from the existing or previously-storedinclination value range. In some embodiments, the receivedstimulus-response signal has the same structure or format as theinclination value range to be modified. Adding the effects of astimulus-response signal is performed using a “(+)” operation(aggregation operation), and removing the effects of a stimulus-responsesignal is performed using a “(−)” operation (disaggregation operation).

In some implementations, a range of inclination values “RI” is definedas follows:

RI={{w _(m),inc_(m) },{x _(m,i)} for i=1,10/inc_(m)}

where w_(m)=a weight of the plurality of weights, inc_(m)=increment, andx_(m) is a value in a range of inclination values from the set of rangesof inclination values, and evidence data “E” (e.g., a digitalrepresentation of a stimulus-response pair) is defined as follows:

E={{w _(E),inc_(E) },{x _(E,i)} for i=1,10/inc_(E)}

where w_(E)=weight, inc_(E)=increment, and x_(E) is a value in the rangeof inclination values, all for E, and modifying RI based on E using a(+) operation is performed as follows:

{{w _(m) +w _(E),min(inc_(m),inc_(E))},{(w _(m) *x _(m,i) +w _(E) *x_(E,i))/(w _(m) +w _(E))} for i=1,10/min(inc_(E),inc_(m))}.

A (−) operation, on the other hand, is performed as follows:

{{w _(m) −w _(E),min(inc_(m),inc_(E))},{(w _(m) *x _(E,i) −w _(E) *x_(E,i))/(w _(m) −w _(E)) for i=1,10/min(inc_(E),inc_(m))}.

FIG. 8A illustrates an example of a time-varying inclination valuerange. As shown in FIG. 8A, moving temporally from left to right, thegraphical representation of the inclination value range changes from (A)an inclination value range having a first, broad, unimodal distributionand centered on a first value (or “peak”) below a midpoint, to (B), aninclination value range having a second, unimodal distribution that istighter/narrower than the first distribution and centered on a secondvalue greater than the first value, to (C), an inclination value rangehaving a third, bimodal distribution with peaks corresponding to thirdand fourth values that are greater than the first and second values, to(D), an inclination value range having a fourth, broad unimodaldistribution similar to the first distribution but centered on thesecond value, to (E), an inclination value range having a fifth,unimodal distribution that is tighter/narrower than the first, second,third and fourth distributions and centered on the first value. Theinclination value range shown to evolve in FIG. 8A can correspond to asingle distribution type, and as such, reflects, for each time slice,only a portion of the composite compressed multidimensional data profile(which, itself, includes a set of inclination value ranges). FIG. 8B isan example of a plot of three evolving inclination value ranges(chronologically, from left to right), which collectively and at anygiven time, can define a compressed multidimensional data profile(“CMDP”), according to an embodiment. At each of time t=1 through t=7, anew stimulus-response signal (or other evidence) is received, and one ormore associated inclination value ranges of the three inclination valueranges shown is modified in response to the stimulus-response signal,which in turn causes an update (or revision) to the CMDP at eachrelevant time period.

The compressed multidimensional data profiles (CMDPs) allow for agreater amount of data to be compressed and stored than would be thecase without such compression. This compression allows for thetranslation processor (e.g., translation processor 103 in FIG. 1) toperform the processes described herein in a much more efficient manner.In addition, this compression allows for the system to scale mucheffectively, to process a greater amount of data, and to performpotentially more accurate and effective processes in that a greateramount of data allows for better feedback into the processes describedherein, which can be updated based on the improved feedback.

It will be appreciated that the above description for clarity hasdescribed embodiments of the disclosure with reference to differentfunctional units and processors. However, it will be apparent that anysuitable distribution of functionality between different functionalunits or processors may be used without detracting from the disclosure.For example, functionality illustrated to be performed by separatesystems may be performed by the same system, and functionalityillustrated to be performed by the same system may be performed byseparate systems. Hence, references to specific functional units may beseen as references to suitable means for providing the describedfunctionality rather than indicative of a strict logical or physicalstructure or organization.

The disclosure may be implemented in any suitable form, includinghardware, software, firmware, or any combination of these. Thedisclosure may optionally be implemented partly as computer softwarerunning on one or more data processors and/or digital signal processors.The elements and components of an embodiment of the disclosure may bephysically, functionally, and logically implemented in any suitable way.Indeed, the functionality may be implemented in a single unit, inmultiple units, or as part of other functional units. As such, thedisclosure may be implemented in a single unit or may be physically andfunctionally distributed between different units and processors.

One skilled in the relevant art will recognize that many possiblemodifications and combinations of the disclosed embodiments can be used,while still employing the same basic underlying mechanisms andmethodologies. The foregoing description, for purposes of explanation,has been written with references to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the disclosure to the precise forms disclosed. Many modificationsand variations can be possible in view of the above teachings. Theembodiments were chosen and described to explain the principles of thedisclosure and their practical applications, and to enable othersskilled in the art to best utilize the disclosure and variousembodiments with various modifications as suited to the particular usecontemplated.

Further, while this specification contains many specifics, these shouldnot be construed as limitations on the scope of what is being claimed orof what may be claimed, but rather as descriptions of features specificto particular embodiments. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

1. A method, comprising: receiving, at a processor, stimulus-responsedata including a digital representation of a stimulus and a digitalrepresentation of a response; identifying, via the processor, adistribution type from a plurality of distribution types, based on thedigital representation of the stimulus; identifying, via the processor,a range of inclination values (“RI”) of the distribution type, based onthe digital representation of the response, the RI defined as:RI={{w _(m),inc_(m) },{x _(m,i)] for i=1,10/inc_(m)}, where subscript“m” is an integer value, w_(m)=a weight from a plurality of weightsassociated with the stimulus-response data, inc_(m)=an increment, andx_(m,i)=a value in a range of inclination values from a set of ranges ofinclination values; and compiling, via the processor, a compressedmultidimensional data profile associated with an object of thestimulus-response data based on the RI.
 2. The method of claim 1,wherein the distribution type is a first distribution type, the RI is afirst RI, the method further comprising: identifying, via the processor,a second distribution type from the plurality of distribution types,based on the digital representation of the stimulus; and identifying,via the processor, a second RI of the second distribution type, based onthe digital representation of the response, wherein compiling thecompressed multidimensional data profile is based on the firstdistribution type, the second distribution type, the first RI and thesecond RI.
 3. The method of claim 1, further comprising: calculating,via the processor, the plurality of weights, the calculating based on arule, the rule being a function of a credibility of a source of thestimulus-response data, the compiling the compressed multidimensionaldata profile being based on the weight.
 4. The method of claim 1,wherein the stimulus-response data is a first stimulus-response data,the stimulus is a first stimulus, the response is a first response, themethod further comprising: receiving, via the processor, a secondstimulus-response data including a digital representation of a secondstimulus and a digital representation of a second response; andmodifying the compressed multidimensional data profile based on thesecond stimulus-response data.
 5. The method of claim 1, wherein the RIhas a multimodal distribution.
 6. The method of claim 1, wherein: the RIrepresents behavioral leanings of the individual; and the compressedmultidimensional data profile is associated with a mindset profile ofthe individual.
 7. The method of claim 1, further comprising: sending,to a compute device of the individual and after compiling the compressedmultidimensional data profile, a signal representing a plurality ofrecommendations for the individual; and receiving updatedstimulus-response data after sending the signal representing theplurality of recommendations for the individual.
 8. A method,comprising: calculating, via a processor, a plurality of weights, eachweight from the plurality of weights being uniquely associated with astimulus-response pair from a plurality of stimulus-response pairs;retrieving, via the processor, a plurality of ranges of inclinationvalues (“RIs”), each RI from the plurality of RIs being uniquelyassociated with a response from a plurality of responses and adistribution type from a plurality of distribution types, eachdistribution type from the plurality of distribution types beingassociated with a stimulus from a plurality of stimuli; compiling, viathe processor, a compressed multidimensional data profile based on theplurality of weights, the plurality of distribution types, and theplurality of ranges of inclination values; and in response to receiving,at the processor, a stimulus-response signal, modifying at least one RIfrom the plurality of RIs according to:RI¹(X,I)=RI⁰(X,I)(+)SD(X,I), where RI⁰(X,I) is a current leaningdistribution, RI¹(X,I) is a modified leaning distribution, SD(X,I) is aleaning distribution of the stimulus-response signal, X is a vectorrepresenting a range of values of a dimension, I is an identifier of anindividual, and (+) represents evidence aggregation.
 9. The method ofclaim 8, wherein the plurality of distribution types includes at leastone distribution of: a desire-to-improve type, a willingness-to-learntype, a judgment-style type, or a learning-style type.
 10. The method ofclaim 8, wherein each RI from the plurality of RIs includes an array ofinteger values between 0 and
 1. 11. The method of claim 8, wherein:RI={{w _(m),inc_(m) },{x _(m,i)} for i=1,10/inc_(m)}, where w_(m)=aweight from the plurality of weights, inc_(m)=an increment, and x_(m,i),is a value in a RI from the plurality of RIs.
 12. The method of claim 8,wherein: the plurality of ranges of inclination values representsbehavioral leanings of the individual; and the compressedmultidimensional data profile is associated with a mindset profiles ofthe user.
 13. The method of claim 8, further comprising: sending, to auser interface and after compiling the compressed multidimensional dataprofile, a signal representing a plurality of recommendations for theindividual; and receiving an updated plurality of stimulus-responsepairs after sending the signal representing the plurality ofrecommendations for the individual.
 14. The method of claim 8, whereinat least one RI from the plurality of RIs has a multimodal distribution.15. The method of claim 8, wherein the calculating the plurality ofweights is based on a credibility of a source of the plurality ofstimulus-response pairs.
 16. A method, comprising: retrieving, via aprocessor, a plurality of distribution types, each distribution typefrom the plurality of distribution types being uniquely associated witha stimulus from a plurality of stimuli; retrieving, via the processor, aplurality of ranges of inclination values (“RIs”), each RI from theplurality of RIs being uniquely associated with a response from aplurality of responses and a distribution type from the plurality ofdistribution types, each RI from the plurality of RIs defined as:RI={{w _(m),inc_(m) },{x _(m,i)] for i=1,10/inc_(m)}, where w_(m)=aweight from a plurality of weights associated with the stimulus-responsedata, inc_(m)=an increment, and x_(m,i)=a value in a range ofinclination values from a set of ranges of inclination values; anddefining, via the processor, a compressed multidimensional data profilebased on a plurality of weights, the plurality of distribution types,and the plurality of ranges of inclination values.
 17. The method ofclaim 16, wherein: the plurality of RIs represents behavioral leaningsof the individual; and the compressed multidimensional data profile isassociated with a mindset profile of the user.
 18. The method of claim16, further comprising: sending, to a user interface and after definingthe compressed multidimensional data profile, a signal representing aplurality of recommendations for the individual; and receiving anupdated plurality of stimulus-response pairs after sending the signalrepresenting the plurality of recommendations for the individual. 19.The method of claim 16, wherein at least one range of inclination valuesfrom the plurality of ranges of inclination values has a multimodaldistribution.
 20. The method of claim 16, wherein the plurality ofweights is associated with a credibility of a source of the plurality ofresponses.